Shahram Yousefi khanghah; Damoon Razmjuee; Somayyie Dehdari; Nasim Arman
Abstract
To better managing of rangeland the vegetation map is one of major factors, because plant communities is planning units of rangeland management and vegetation map shows the current status of plant communities. This research was conducted to produce vegetation type's map using Landsat 8 image classification ...
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To better managing of rangeland the vegetation map is one of major factors, because plant communities is planning units of rangeland management and vegetation map shows the current status of plant communities. This research was conducted to produce vegetation type's map using Landsat 8 image classification in Behbahan, Khuzestan province. Rangelands of the study region is warm semi steppe and winter grazing. Geometric correction of satellite image was performed by ground control points with an error of less than one pixel. Atmospheric correction of existing data using the dark object subtraction was done. Field visits for vegetation type's border controlling and sampling training area was conducted. Eight supervised classification algorithms included Parallelepiped (PP), Minimum Distance to mean (MD), Mahalanobis distance (MAH), Maximum Likelihood (ML), Neural Net (NN) and Support Vector Machine (SVM) was performed. The results showed that ML algorithm has the highest overall accuracy (87.5 percent) and kappa (0.867) and PP algorithm has the lowest overall accuracy (67.1 percent) and kappa (0.571). It is suggested that, along with digital methods of classification of satellite images, visual interpretation should be used to clarify the boundary of the obtained vegetation types map.
somaieh dehdari; nezam armand; mohammad faraji; nasim arman; fatemeh hadian
Abstract
The study aimed to evaluate the effects of Karon 3 and 4 dams on land use and cover changes, for this aim 4 images over 28 years (taken in 1985, 2003, 2009 and 2013) were obtained and geometric, atmospheric and topographic corrections were applied. Maximum likelihood and post-classification techniques ...
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The study aimed to evaluate the effects of Karon 3 and 4 dams on land use and cover changes, for this aim 4 images over 28 years (taken in 1985, 2003, 2009 and 2013) were obtained and geometric, atmospheric and topographic corrections were applied. Maximum likelihood and post-classification techniques were used to detect land use/cover changes and their accuracy then was assessed using topographic maps and field works. The overall classification accuracy and Kappa statistics for all the maps were more than 0.79 and 86.24% respectively. The classification map of year 2009 indicated that about due to Karron3 dam and 6734.88 hectares because of rangeland and forest were destroyed. Classificated map of 2013 indicated about 5127.39 hectares increased because of Karon 4 too. The overall findings of this study indicate that forest and range land degradation in the region, is due to the construction of Karun 3 and 4.
